On the other hand, few-shot imitation learning has been a promising paradigm for generalization. In the few-shot imitation learning setting, an imitation learning policy is trained on an offline dataset of demonstrations and is then adapted in few-shot to a downstream task [1]}. Few-shot imitation learning has the added advantage over RL in that it is often easier for a human to provide a handful of demonstrations than it is to engineer a new reward function for a downstream task. However, unlike RL with behavioral priors, few-shot imitation learning is most often limited to short-horizon problems. The reason is that imitation learning policies quickly drift away from the demonstrations due to error accumulation [2]}, and especially so in the few-shot setting when only a handful of demonstrations are provided.
